
# initializing
from SOM import SOM
from dataloader import DataLoader
import numpy as np


CO2_data = DataLoader(data_path='co2-emissions-per-country.csv')
CO2_data.load_data()


# Data is organized according to the year
CO2_data.data.keys()

dict_keys(['1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1916', '1917', '1918', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1887', '1888', '1889', '1890', '1891', '1892', '1893', '1894', '1895', '1896', '1897', '1898', '1899', '1900', '1901', '1902', '1903', '1904', '1905', '1906', '1907', '1908', '1909', '1910', '1911', '1912', '1913', '1914', '1915', '1830', '1831', '1832', '1833', '1850', '1855', '1858', '1859', '1860', '1861', '1862', '1863', '1864', '1865', '1866', '1867', '1868', '1869', '1870', '1871', '1872', '1873', '1874', '1875', '1876', '1877', '1878', '1879', '1880', '1881', '1882', '1883', '1884', '1885', '1886', '1807', '1819', '1820', '1821', '1822', '1823', '1824', '1825', '1826', '1827', '1828', '1829', '1834', '1835', '1836', '1837', '1838', '1839', '1840', '1841', '1842', '1843', '1844', '1845', '1846', '1847', '1848', '1849', '1851', '1852', '1853', '1854', '1856', '1857', '1802', '1785', '1786', '1787', '1788', '1789', '1790', '1791', '1792', '1793', '1794', '1795', '1796', '1797', '1798', '1799', '1800', '1801', '1803', '1804', '1805', '1806', '1808', '1809', '1810', '1811', '1812', '1813', '1814', '1815', '1816', '1817', '1818', '1750', '1751', '1752', '1753', '1754', '1755', '1756', '1757', '1758', '1759', '1760', '1761', '1762', '1763', '1764', '1765', '1766', '1767', '1768', '1769', '1770', '1771', '1772', '1773', '1774', '1775', '1776', '1777', '1778', '1779', '1780', '1781', '1782', '1783', '1784'])


year_2020 = np.array(CO2_data.data['2020'])
num_rows = year_2020.shape[0]
num_cols = year_2020.shape[1]

print("Data Attributes: ", num_cols)
print("Countries: ", num_rows)

Data Attributes:  4
Countries:  214


year_2020[0:10]

array([['AFG', '1.2160286', '0.03', '0.3124'],
       ['ALB', '0.4534673', '0.01', '1.5757'],
       ['DZA', '15.499546', '0.45', '3.5346'],
       ['AND', '0.0466294', '0.0', '6.035'],
       ['AGO', '2.2198161', '0.06', '0.6754'],
       ['AIA', '0.0123426', '0.0', '8.2273'],
       ['ATG', '0.043041', '0.0', '1.0285'],
       ['ARG', '15.6978063', '0.45', '0.0186'],
       ['ARM', '0.5890292', '0.02', '10.8692'],
       ['ABW', '0.0753218', '0.0', '0.0507']], dtype='<U32')


for key in list(CO2_data.data.keys()):
    print("Year: ", key)
    year = np.array(CO2_data.data[key])
    num_rows = year.shape[0]
    num_cols = year.shape[1]
    # for better results size of the map must be of the same size as labels
    map_size = (15, 15, num_cols-1)
    som = SOM(map_size=map_size ,learning_rate=0.1,epochs=1000)
    som.train(year, with_labels=True, with_plot=False)
    som.plot_map(save=True, filename="CO2_map"+key+".png")
    som.save_map(filename="Maps/CO2_map"+key+".json")

Year:  1949
(103, 3)
Started training...

100%|██████████| 1000/1000 [00:14<00:00, 69.47it/s]

File Saved!
Year:  1950
(163, 3)
Started training...

100%|██████████| 1000/1000 [00:25<00:00, 39.91it/s]

File Saved!
Year:  1951
(164, 3)
Started training...

100%|██████████| 1000/1000 [00:22<00:00, 43.83it/s]

File Saved!
Year:  1952
(164, 3)
Started training...

100%|██████████| 1000/1000 [00:26<00:00, 38.03it/s]

File Saved!
Year:  1953
(165, 3)
Started training...

100%|██████████| 1000/1000 [00:26<00:00, 37.48it/s]

Self Organizing Map¶